EVERY SPOKEN WORD
35 min read · 7,219 words- 0:00 – 1:06
Intro
- GTGarry Tan
Frankly, some of the most powerful and badass founders that we've been seeing lately, they're- might be in their late 30s, 40s, even 50s. I mean, there's a sort of resurgence of the experienced founder. A lot of people seem to say that they wanna be, uh, YC for solo founders, but it turns out YC is the YC for solo founders. What a w- weird moment we are in history where you wake up in the morning, you, like, wire up a new model, and then these things that even a month ago you were just like, "Why isn't it working?" It just starts working. [upbeat music] Welcome back to another episode of The Lightcone. At YC, we work with thousands of founders per year, which means we start to see things before they're obvious. So we wanted to share some of that with you today. What's the state of the art, and what's coming next? What should you, the builder, know? Let's get started. Diana, you have a few things to share with us. [chuckles]
- 1:06 – 3:29
Startups Are Moving From Bits to Atoms
- DHDiana Hu
So we did a bit of a analysis for all the companies we accepted in the last 18, 12 months, and we have some pretty shocking stats to share with everyone. So one of the big ones is the number of Hard Tech companies that are in the batch. It has gone from 8% to 20%. There's a lot of, uh, underlying reasons why that has happened. We will go deeper into that. The other one is, uh, the rate of growth of companies and what YC does to the companies has accelerated. So the median YC company, when it gets accepted, is at zero in revenue. It's pre-revenue, pre-product. And by the end of the batch, in the past, companies would get to about 8K median revenue, and now the companies in median are getting to 20,000 monthly revenue as opposed to 8K. So those are the top two that we can dive deeper into.
- JFJared Friedman
Yeah, let's dig into Hard Tech first. Like, what are these Hard Tech companies, and what's, what's driving this?
- GTGarry Tan
Things that actually touch atoms and not just bits.
- JFJared Friedman
Yeah. And I- I- I think you have the- the- the category breakdown of the Hard Tech companies, right, Diana?
- DHDiana Hu
Yeah. So specifically, robotics has been a big one. It has gone from 1% of the batch to about six, 7% of the batch. Industrial manufacturing, building things back in the US has been a huge trend. It has, it has gone from about 4% to 10% of the batch. The other one is defense, is a big one. We all have been work with a lot of, uh, defense startup. It has gone from about 1.5% to about 5% of the batch. The other big one is there's this, uh, compute need that the world is getting into with AI. So there's a lot of companies building the semiconductor stack or photonics. It has gone from about 1% of the batch from a year ago to about close to 4% of the batch. And the other one even below the stack of compute is power. So there's a lot of power infrastructure as well. It's gone from also 1% to about close to 3% of the batch. So all these numbers across the physical atom stacks have somewhere triple or quintupled.
- GTGarry Tan
Yeah. This is the, uh, age of the machine, I think.
- DHDiana Hu
Mm-hmm.
- GTGarry Tan
And that's-- I mean, as the world goes, you know, our, our motto, um, the T-shirt says, "Make something people want," and people sure do want those things right now.
- 3:29 – 5:21
Why AI Is Making Hard Tech Easier
- DHDiana Hu
And the other interesting factor about all these companies that are going deep into atoms is that we've been funding more technical founders and with more expertise than ever, right, Jared? We have this fun stat about the current summer batch with-
- JFJared Friedman
In the current summer batch, one in six of the founders actually has a PhD. It's way more than that's been historically, and it's because, yeah, if you're doing, you know, something with, like, silicon photonics, you're probably gonna need a pretty strong r- research background in that. And so we've been funding a lot more of those founders, and those founders, I think, have disproportionately been doing, like, especially well.
- GTGarry Tan
I think AGI compounds this in a really fascinating and awesome way in that, like, you might think in the past you actually... Like, Hard Tech was hard because you had supply chains. You had, uh, an incredible software component o- often. Uh, I think Palmer Luckey talked about this a lot when it came to Anduril. It's like having code gen means that suddenly even all the things that they do at Anduril, uh, can happen much, much faster, right? Even three or four years ago, you would talk about software engineering and, like, the top-tier software engineers as one of, one of the limiting reagents to being able to do really, really top-tier, full-stack, uh, hardware, and that's less and less true. I mean, you still need one or two of them or you need, like, a small team, but you don't need to hire 1,000 great engineers versus Google or Meta or whoever else, and that really changes the economics.
- JFJared Friedman
I mean, that's the, that's the true bull case for Hard Tech. It's that it's not just that people are shying away from funding software businesses, but it's actually that the super smart models that we have now are actually accelerating scientific research and making it possible for startups to have bigger research breakthroughs earlier, and that therefore these di- deep tech companies will actually work better.
- 5:21 – 9:48
Defense, Manufacturing, and the Return of Hard Tech
- DHDiana Hu
I think the other factor is, um, there's basically three macro trends that are also driving all this, all this growth on atoms and is seeing huge companies like SpaceX have such a successful IPO, has created a generation of founders wanting to build in space. So it's lots of these companies that are building across the whole stack. So there's been companies, uh, in the, in the current batch in summer '26. There's this company that we work with called Exosat that's trying to build basically a sovereign Starlink solution. There's other company that I work with, uh, in winter '26 called Beyond Reach Labs that's building solar panels for satellites in space. If you imagine companies like Starcloud wanting to Have all these data center in space, they will need to have power, so this is a obvious solution. Now, the other macro trend is, um, I think we have a generation of current founders right now that have, uh, grown with the war that's been very front and center and spoken a lot in social media-
- GTGarry Tan
Yeah
- DHDiana Hu
... and they want to do something.
- GTGarry Tan
Like, two, two of the companies I'm most excited about that I funded the last couple batches, one was Icarus last fall, and then Nine Mothers this last spring, and both of them were defense. Icarus is doing, like, a solar-powered U-2 spy plane that gives overwatch and can also do comms, which is actually really important. The future of drone war is being able to actually communicate with your drones on the ground and see what's going on. They've been able to get to seven-figure contracts with the new Department of War. Um, and then likewise with drone war, um, Special Forces has been buying, uh, Nine Mothers anti-drone defense. So it's basically a shotgun turret with CV, uh, but it's actually almost the only way that you could protect Special Forces, uh, deep behind en- enemy lines. I mean, these are people who have been training for years and years, uh, you know, in a very elite special force that, um, you know, America doesn't have, uh, you know, thousands of these people, you know? We, we have a very, very small set, and so protecting them from, uh, what could be, like, a commodity drone attack is actually really existential for the Department of War. So just really cool to see this new administration actually, uh, approach defense in a very different way. Like, it, you know, classically, there was just a lot of, frankly, capture from the big defense primes that are just doing sort of, um, cost plus. They, they think of themselves as consultants. And, you know, to be able to see new startups that can actually take advantage of all of the AI, all of the tech, all of the new ways of building things to build things that, frankly, uh, the defense primes can't build, you know, that's a really powerful mega trend right now.
- DHDiana Hu
Now, the thing about defense is not just those full solutions that get sold to the government. There's also a lot of category of startups that are dual use that they sell both to the private sector and to the government and that have to do with everything down the supply chain, so things like manufacturing things back in America, building custom, um... I think you had this company, Knox Metal.
- GTGarry Tan
Yeah, they're bringing metal manufacturing back to America. America has, like, largely lost its metal industry. It got hollowed out over the last few decades and can't build stuff without metal. And so Knox Metals is, like, rebuilding America's metal supply chain. A- and they're doing it in, in the heartland of America in, in, in Detroit, where there's all these, like, empty factories that have basically just been, like, sitting there.
- HTHarj Taggar
Uh, and they're an example of, like, the trend where the-- it's not just people are doing hardware companies, but the hardware companies themselves are growing faster than ever. Like, and I think I saw a PG tweet that Knox Metals is growing, like, at software growth rates.
- GTGarry Tan
Growth rates, yeah.
- HTHarj Taggar
Do, do you understand that? Like, kind of what-- how are they growing so fast?
- GTGarry Tan
So one reason is that a lot of their customers are these new defense tech startups that have sprung up and need metal to build all their, all their stuff, and the existing suppliers that are these sort of, like, sleepy old businesses mostly run by old people just, like, can't keep up with the pace that the new defense tech startups want to build at. And it reminds me a bit of, like, when the Web 2.0 boom happened early in, in the YC days. We would have these new startups, but then they would prefer to buy from new startups that could sort of, like, move at their speed and, like, work well with them, like Stripe, for example. You could use a legacy credit card vendor, but, like, it's just, like, way better to work with Stripe. And so I feel like they're sort of becoming that for the whole defense tech ecosystem.
- 9:48 – 12:34
AI Compute Is Becoming a Physical Infrastructure Problem
- DHDiana Hu
Now, the third trend is, um, basically compute is a very heavy physical atoms process to get all these data centers live very quickly because a lot of the demand for AI that we've been talking has been skyrocketing. And there's a very interesting stat where GPUs from Nvidia, let's say like a A100 GPU per hour, is actually appreciating in cost-
- GTGarry Tan
Hmm
- DHDiana Hu
... which is unusual. In the past, when you get a, A- A100s by now are sort of old.
- GTGarry Tan
They're pretty old, yeah.
- DHDiana Hu
The price is going up because there's just too much demand and not enough supply in compute. So there's a lot of, uh, startups that are now working on bringing data centers live, and you have everything from the construction of the sites to the software to plan it, to actually doing the data c- center build-out, to inter- interesting solutions that have to do with how to power them and combination of energy, battery. So there's all these category of startups, and even to the point of, uh, going down to the core compute silicons. There's a number of, uh, startups that are building new, new silicon for an alternative to, to, to Nvidia. There's this company that Tyler worked with called Lamb Labs that's building new processors for compute. There's another one that I'm working on this batch called Bot that is trying to build basically new custom hardware architecture that's using ternary representation for models because what it turns out, which is a funny trend right now, if you look at all the Nvidia architectures from A100s to H100s and now the B, B300s, each of these generations, they're actually going down in floating point precision in terms of, uh, what they were. They're going from FP32, 16, 8, et cetera. And it turns out that the LLM architecture doesn't need the full precision floating point.
- GTGarry Tan
FP2 is even somewhat usable.
- DHDiana Hu
Right. So this is what Bot is trying to do. And I think you have an interesting one that's doing, um, the interconnect with, uh, with photonics.
- GTGarry Tan
Yeah. There's a company called Dipole Labs in the current batch that is replacing the switches that are in data centers, which are essentially the, like, routing systems between d- different GPUs. If, like, GPU one-- A wants to talk to GPU B, they talk to each other through this device that's called a switch, and these switches right now are electronic. And so there's actually an issue, which is, like, the switches are not keeping up with the GPUs. The, the speed of the GPUs keeps going up, and the switches are actually the bottleneck for many data centers and b- Any different workloads. And so Dipol Labs is building the first fully optical switch where it's like all photons from GPU A all the way to GPU B. And so it will actually be much faster than the electronic switches that we, we use now.
- DHDiana Hu
Now, the last one that's driving
- 12:34 – 15:40
Robotics Is Approaching Its ChatGPT Moment
- DHDiana Hu
all this, uh, move to atoms is this, uh, aspect where robotics is gonna happen. So there's a lot of companies building the stack around that, and everything from vertical robotics in specific industries, to the infrastructure to deploy robots, to, uh, data selling to the new robotics labs. Because it- there's this moment that everyone in, in the industry is feeling that we're gonna get to the ChatGPT moment. It's not quite there yet, and w- I think we're figuring out a new, uh, scaling law around it. So there's a lot of that. And we had Quan here a couple episodes ago, and we're believers that that's gonna happen. I mean, robotics-
- JFJared Friedman
Yeah, from Pi.
- DHDiana Hu
From Pi, right? Half is AI and half is, uh, hardware. And then-
- JFJared Friedman
I was hearing this, reading this morning that even Astra is, like, a big leap forward for, for robotics, like, on, like... I forget the benchmark, but there's a benchmark where, like, Fable was maybe at 10%, and, um, Astra is showing, like, you can do, like, 60% to 70% of the tasks.
- GTGarry Tan
So data to just, you know, wake up in, you know, another couple weeks, and another breakthrough happens, and we're a little bit closer. [laughs]
- JFJared Friedman
It's been really cool for me to see the resurgence of Hard Tech because, you know, at YC we've been funding Hard Tech companies since 2014. That's really when we w- we started. But it was, like, pretty hard to get these companies funded before. Like, I remember, like, pre, pre this recent resurgence, we would, like, fund awesome stuff that we were super excited, like rockets and planes and chips and data centers and stuff like that, and then, like, VCs would just be like, "Ah, we only do B2B SaaS." Um, [laughs] and it's hard to bootstrap a company like this, so you really do need, like, downstream investors who can fund lots of... Who, who, who can, who can fund, you know, a, a full, a, a full capital build-out. And so it's cool that, like, it seems like Silicon Valley, which historically, like, venture was set up to fund Hard Tech, but it, like, drifted away from it for a decade or two because it was so profitable to just fund SaaS companies, and so it's cool to have it coming back to its roots. Yeah, on the point about, like, the, the peer investors wanting to do Hard Tech again, it does seem like that... I've never seen that happen so quickly. I mean, d- it seemed like it happened pretty immediately when, like, SaaS stocks were down earlier this year, Claude Code was surging, and that just became, like, the, the... I mean, I feel like even at Demo Day, it literally happened. I feel like that happened probably mid the winter batch at the start of this year, and it seemed by even Demo Day that investors were starting to be a lot more interested in Hard Tech companies, and that's just extrapolated. I mean, it is worth noting, though, on the other side, like, since that, a bunch of the SaaS stocks have actually recovered and are doing better than ever. Like, Salesforce is, like, the prime example of that. I think Snowflake recently had, like, like, two days ago, had these, like, blow-out earnings. And so it's possible that-
- GTGarry Tan
It all hits.
- JFJared Friedman
Yeah, maybe. I mean, that would be the dream case.
- GTGarry Tan
[laughs]
- JFJared Friedman
Um, I mean, I still think we're seeing real stuff, though. Like, it clearly- the software that gets built in the future and what's valuable is different. Like, it just has to be. And so partly it seems like what we're seeing with Salesforce is the classic, the system of record argument is actually playing out.
- GTGarry Tan
The moats are intact for now.
- JFJared Friedman
Yeah.
- 15:40 – 17:59
Software Isn’t Dead. It’s Becoming the Harness
- JFJared Friedman
Like, if you have a thing that agents can use, um, that is actually valuable, and if anything, you'll just... Like, agents will use software a lot more than humans will, and that seems to be driving Salesforce growth. And so kind of takes us back to the other trend that we've talked a little bit about is if you think of agents as your customers, and you make things that agents want, and your software is something that agents want to use, then that seems like the right type of software.
- GTGarry Tan
Yeah, Salesforce is super interesting because, uh, I think they started releasing their own Slack harness, Slack AI harness, and so I think we're right at the beginning of, like, the next AI harness wars. It's like Codex wants to be it, Claude Code wants to be it, um-
- JFJared Friedman
Open Code
- GTGarry Tan
... Open Claude could be it, Hermes, Open Code. It seems like there are going to be a bunch of them, and it's not gonna be quite like the browser wars in that the browser wars tend toward, like, one winner. But, you know, I guess it's anyone's guess. And then, um, yeah, Benioff has a pretty big advantage in that, you know, a lot of the most AI-appealed people and companies in the world still use Slack. And, you know, if the harness is in there and it's your, your system of record for how people collaborate, then you have, like, this mega data moat. And then, you know, SaaS can still be as valuable as it's ever been valued if those moats hold.
- JFJared Friedman
Yeah, I thought you had a really interesting tweet maybe a week or so ago about how the software or system of record companies will have to become, like, harnesses effectively.
- GTGarry Tan
Yeah, they have- I mean, and that's bas- that was about Slack-
- JFJared Friedman
Yeah
- GTGarry Tan
... I would say. It's like, basically, if you are a system of record, you either will be preyed upon, like you'll release an MCP and then maybe, like, the data, you know, you lose your moat around the data, the data goes elsewhere, like, becomes very trivial to switch, or you kind of have to be a harness. You have to be the way people not just read and write, but actually do their work inside, you know, your system of record.
- JFJared Friedman
And get the most value out of it. I mean, it's a lit- it's a little bit like the model companies. Like, was the, um, was it, uh, RKGI was the benchmark where... Or there was a benchmark where the, the-
- DHDiana Hu
RKGI V3.
- JFJared Friedman
Yeah, where the pre- or pre-Astra ChatGPT model didn't-
- DHDiana Hu
Yeah
- JFJared Friedman
... do as well, but then they say, "Well, that's just 'cause it was plugged into the wrong harness."
- GTGarry Tan
Yeah. [laughs]
- JFJared Friedman
So it's like the model plus the harness gets you the output.
- GTGarry Tan
Oh, yeah. I mean, uh, with a custom harness, they claim Astra got to ni- north of 90% on RKGI 3.
- DHDiana Hu
Yes. I think a couple months ago this was in the low two digits-
- GTGarry Tan
Right
- DHDiana Hu
... which is an impressive
- 17:59 – 22:52
Why AI Startups Are Growing Faster
- DHDiana Hu
leap. And I think you have a very good point around software. It's not that software and SaaS is dead, what people claim on the internet, it's just that it's, has transformed. We're actually seeing this in the batch. The percentage of companies we accepted that do sort of full stack end-to-end work or a task has gone from just 10% to over 25% of the batch. This has to do with actually doing the job. The agent does the job, not just like a point solution, which old SaaS in Five, eight years ago was just like a point solution, and you needed a, you needed someone to operate the SaaS software. Right now, it just runs by itself. And actually, in the batch, this is where we're seeing a lot of the growth in revenue. I think I gave the stat of the median, uh, startup when it gets into YC is at zero in revenue, and it has gone from, by the end of the batch, it was about 8K in MRR. Now it's like about 20K in MRR, and a lot of these are-
- GTGarry Tan
That's a huge jump.
- DHDiana Hu
It's a huge jump.
- GTGarry Tan
That's like non-trivially big jump for the median, right?
- DHDiana Hu
Mm-hmm.
- GTGarry Tan
The average is even higher.
- DHDiana Hu
And it has to do with doing the full end-to-end job with, for example, doing insurance broker, actually doing the clinical intake, doing the full end-to-end workflow of, I don't know, medical billing, et cetera. And these are the ones that are growing a lot, and I think there's another factor where that's happened. I think we talked about this in couple episode ago. We right now are about almost a year since agentic coding started to work, since Opus 4.5, that we're seeing these workflows fully blossom, and the result are basically people want their job just be done and are willing to buy software that just gets the job done.
- JFJared Friedman
I think when people hear these revenue numbers growing so fast, an easy knock on it is, like, maybe it's just AI hype, and these companies are just, like, shelling out money for AI products because it's, like, the cool thing to do. And to be fair, that's probably some of that. But I think, like, the bull case is actually something that we said in an episode, like, two years ago when agents were really just beginning to be a thing, where we were like, "Actually, the products are just gonna be more valuable." Like, if they automate the whole job, they will actually just be more valuable than some, like, s- system of record that tracks the job but doesn't do the job, and therefore, companies will just, like, pay more money for the, for, for, for their product. And I definitely see that in companies that I work with where, yeah, they just go to a company, and, like, the value proposition is so great that, like, large enterprises are willing to write big checks very, very early.
- HTHarj Taggar
Yeah, a company that I'm seeing, um, having this effect is Juicebox, an AI recruiting tool. Like-
- JFJared Friedman
Oh, yeah
- HTHarj Taggar
... it's been on an incredible growth rate for, like, the last couple of years now. But they'd started out as... I mean, I would say it was essentially sort of LLM-powered people search. Like, the thing that they did was you could type in sort of the spec of the type of person you wanted to hire, and it did a really good job of pulling the, um, good profiles of people that you may want to contact. Um, but then you, you still have to go and contact the people. And recently they've launched an agent product, which is really taking off, and the agent, like, doesn't just search for the people. It, like, contacts the people. Event- it'll be able to schedule the interview, do a bunch of things. Um-
- JFJared Friedman
That sounds awesome.
- HTHarj Taggar
Yeah, and they're seeing that that's gonna, just on a, like, per-account basis, I think is gonna double or triple, like, the revenue they make from a single customer because customers want more and more of these agents. I don't think it's fair to say that it's like... It's not like it's, like, automating the job of the recruiter at all. It's just, like, it's just changing it. Like, it, like, the recruiters didn't necessarily want to be doing, like, that sort of rote reach out to, like, 500 people anyway. Like, the thing that makes the recruiter job, I would say, like, more skilled and interesting is, like, there's, like, culture fit. That's just gonna be really hard for, like, an AI to do a phone screen that assesses, like, how well someone's gonna be, like, a culture fit and, um, and the human element of it. And so I think they're finding that the recruiters themselves are actually really excited to use the agents because it frees them up to do the work that they feel is, like, unique and interesting.
- DHDiana Hu
The other shocking stat is that the companies that really accelerate during the batch, they really start taking off. One of the things that we started experiencing this year that we never experienced in the past is we have companies breaking from zero to seven figures in revenue during the batch, and that is in a span of three months, and that's shocking. In the past, that would have taken, for a company to get to that, about 18 month or more, and they're doing it in that amount of time. Part of it is they're solving real problems, and because of agentic coding, they're actually building products that are a lot more mature as well, and they're able... These founders that are super AI-pilled run, I don't know, 20 coding agent sessions to get to that product maturity.
- JFJared Friedman
And there's another
- 22:52 – 27:08
The Hidden Boom in Data and RL Environments
- JFJared Friedman
category of companies that's also been growing super fast recently, which is companies that sell data or RL environments to the labs.
- GTGarry Tan
Mm-hmm.
- JFJared Friedman
This one might be interesting to talk about because a, a lot of these companies are pretty stealthy. They tend to have a disincentive to talk about how well they're doing instead... you know, compared to most companies that like to talk about how well they're doing. And so I think people out there might not realize how big a category this has become. When YC funded Scale back in 2016, this was, like, a tiny little niche thing. It wasn't even a category. There was... Initially, it was basically just Scale who was doing it, and then Mercoi began to do it, and then, like, a couple other companies. But in the last couple years, it's become a big category. We pulled the data recently, and, um, just in the last two years, YC has funded more than a dozen companies that are each making more than $10 million a year selling data or RL environments to the labs.
- GTGarry Tan
And in many cases, hundreds of millions of dollars.
- JFJared Friedman
And in many cases, hundred- hundreds of millions of dollars, and these are companies that were just, just a couple of years old.
- GTGarry Tan
That's pretty-
- JFJared Friedman
Yeah
- GTGarry Tan
... fast to revenue, honestly.
- JFJared Friedman
Yeah, it's, like, pretty bananas. I... Do you wanna talk about any of them, Garry?
- GTGarry Tan
Uh, I mean, the big ones, I mean, I think AfterQuery and DataCurve both really, really great. I mean, there are probably too many to name that are honestly, like, maybe don't even wanna be mentioned because-
- JFJared Friedman
[laughs]
- GTGarry Tan
... well, you know, once you have something that's working, you almost don't want people to know. I think that it's kind of natural to understand this, though. I mean, data is one of the legs of the scaling law, and, you know, much has been made of compute, but without the data, how are you gonna make these models that much better? Um, the RL environment thing is interesting. I mean, there's a lot there. I mean, the, there's a lot of, like, pure customization that's happening for specific use cases. Like, you, you'll have, like, R- RL environments for finance, for instance, and someone can go very, very infinitely deep with that, and it's, like, a little bit of expertise. It's a bunch of computer science. It's some systems work. But RL seems to be I mean, one of the big engines for how... I mean, people are maybe bench- benchmark maxing a little bit more than they should, but, uh, it costs money to do it, and it's seemingly here to stay in terms of how big model companies are gonna approach it.
- DHDiana Hu
Reportedly, the big labs are spending about a billion dollars on this. It's not a very known fact, but there's, there's actually a real business to be built around this. And RL Environments is the current flavor it, and there's things with long-term horizon paths that are getting built up, and I think that is starting to also emerge in robotics. The labs also wanna solve the problem of getting AI to work on the physical world, so they need a lot of the environments in the real world. So things with egocentric data, teleop, robotic tasks, starting to emerge as, like, big data category where labs are spending eight, nine-figure deals with these companies. We had a number of companies in the batch that work on that and been able to close, close revenues in that, in that space. It's companies like, um, in the current batch of Summer '26, there's Praxis Robotics. There's one that I'm working with that has, like, a network of places across the world where industrial manufacturing gets done. They collect data from that. There's this other company that Brad worked with called Deep Reach that also has data that local entrepreneurs in- across the world do.
- JFJared Friedman
And Human Archive in W- Winter '26. Yeah, there have been a bunch of these companies recently.
- DHDiana Hu
Right.
- GTGarry Tan
I think, like, if I were gonna prognosticate, like, one of the things going back to the, uh, you know, all systems of record need to be AI harnesses, they might also need to start training their own models, and that's where things like River AI or Tinker start becoming really interesting. Out of the box, like, you can sit there in Claude Code or even Open... I use OpenClaude to train my own models, which is very fun. It'll do its own data cleaning and everything. But to date, like, that hasn't been a huge factor, but I can see that becoming a much, much bigger factor. I mean, when you have proprietary data, and you can train... I mean, the open s- open weight models are, uh, really nearly frontier. If you can, like, sort of special purpose train these things to do, um, even better than what the frontier can do, like, that, that's gonna be re- really, really powerful.
- DHDiana Hu
I think this is actually gonna be
- 27:08 – 29:38
Why Robotics Will Need Specialized Models
- DHDiana Hu
even bigger in robotics. I mean, this is a hypothesis. It's not proven yet. But robotic foundation models in robotics, I think, uh, have, uh, very different characteristics versus LLMs. LLM is, the whole thing is you model reality as language, and for robotics, you model reality in the physical 3D space, which has way more degrees of freedom. And perhaps in order to get robots to work in a specific vertical, like, let's say robots that do operations in data centers. I have this company called Boost Robotic that build robots for data centers, like doing the cabling. It is possible for these robots to work. It is, it's better to get a model that's fine-tuned and trained on custom data that just works in that environment because the, the thing that's also challenging for robotics, they need to be in real time and respond very quickly to, to the stimuli and have a action plan, which is different than LLMs. LLMs, you can have this, this feature where you can just let it go and come back. But for robotics, you can't be- because if, if, I don't know, let's say you connect that cable to, to data center, and then someone comes in and, like, knocks the robot out, and things could get connected to the wrong plug, let's say.
- JFJared Friedman
Yeah, my understanding is that all the YC companies that are using physical intelligence as models to deploy robotics, they're all fine-tuning the PI models. I don't think any of them are able to use the PI models out of the box. Even though it's a great, like, starting point, you have to actually fine-tune it for, like, your specific case, like data center cables, in order for it to work.
- DHDiana Hu
Mm-hmm. You work with this company, uh, Ultra, right?
- JFJared Friedman
Yeah. They start with a PI model, but then they have, like, thousands of hours of footage of, like, putting things in boxes that makes it really good at pu- putting things in boxes.
- GTGarry Tan
I've heard the argument basically that, you know, you could look at Claude Code, like Claude Code can use its mo- its, uh, code transcripts to figure out who the top coders are, and you can take that and turn it around and, you know, basically train the next coding model to be even better. If you happen to own TikTok, you happen to have all of the data on, uh, what people watch and click on and what's compelling, and you can use that to make much more compelling videos in Seed Dance. So, you know, that's already been happening. I just, you know, I think that that, that trend is gonna continue in a fairly spectacular way from here. So one of the things that we've been noticing, I think all of us have, is that frankly, some of the most powerful and badass founders that we've been seeing lately, they're might be in their late 30s, 40s, even 50s. I mean, there's a sort of resurgence of the experienced founder. A lot of people seem
- 29:38 – 32:54
The Rise of the Solo Founder
- GTGarry Tan
to say that they wanna be, uh, YC for solo founders, but it turns out YC is the YC for solo founders. Diana, you have a few stats that, uh, y- you found surprising.
- DHDiana Hu
One of the shocking stats from analyzing the accepted companies from a year ago, we used to only have about 5% of the companies accepted be solo founders, and now we're over 18, 19%, which is a huge... This is the highest spike that we've seen.
- GTGarry Tan
Yeah. Almost one-fifth of the batch, so... And it seems like it's gonna keep going. You know, before you had, you had to have, like, you know, so many different skills. You had to be a great hustler. You know, you had to be able to explain and, you know, we would say, like, they have to be good talkers, right? [chuckles] Like, you need someone who can, you know, uh, be a hot person. [laughs] You need someone who can actually come in and convince someone of something. Uh, and then if you paired that with someone who is a world-class technologist, that's sort of the combo that is so ideal. Um, and so classically, you would need co-founders to do that. Like, you know, any, it, you didn't necessarily need one, but, like, it would increase your chances by so, so much. And I feel like a lot of that is, like, changing to this degree. It's becoming such that, like, knowing what to prompt and knowing what to build is so much more difficult and valuable than just knowing, you know, the CTO being able to code the thing.
- HTHarj Taggar
I think what's going on is that we've always actually had, um, It's hugely successful single founders. I think people don't realize that this about YC. There's different sort of definitions of it, but for all intents and purposes, Apuva with Instacart, Brian Armstrong with Coinbase, at least when the batch started, were single founders.
- GTGarry Tan
Yeah, Parker Conrad got into YC as a single founder, and then I, uh, interviewed Laksh Srini, his- who ended up being his CTO.
- HTHarj Taggar
The bar for being able to, like, have the idea, be able to sell it, and be able to build it all by yourself, which is really, really high, um, and like-
- GTGarry Tan
And that's actually totally doable.
- HTHarj Taggar
Yeah, I think that's what's going on. Like, in that case, like, those three are just, like, incredibly exceptional people, and there's just, like, very, very few people who are capable of that, and now you can actually, like, get going. And so I think you just don't have to be quite that, like, exceptional, at least on one of those dimensions, the building part, to be able to get going.
- GTGarry Tan
But net-net, it's still valuable to have co-founders.
- HTHarj Taggar
Yeah, well, you-
- GTGarry Tan
It's still a measure of, like, you know, if your co-founders are super elite, like, that means you're probably super elite, and it just increases the chance of success by a lot, and-
- HTHarj Taggar
Well, in, in each of those cases, they did bring on co-founders. I think in each of those cases, you just get going, and they got traction, and then they added on co-founders sort of at a certain point. And so maybe, like, the, um, the equity ownership is different, or maybe the dynamic is just slightly different to the traditional, "Hey," like, you're, you start out and, like, the two of you in a room and, uh, and you're completely 50/50. I don't even want to put the equity stuff in, but-
- JFJared Friedman
I, I, I've, uh... Yeah, I don't have the stats, but I have definitely seen a greater trend towards that, people adding co-founders later in the company life cycle after the thing has already, like, gotten off the ground.
- HTHarj Taggar
I think that will be the trend. I think we'll see a little, like, more single founders in the batch, which we're already seeing, like, starting the batch. But at least of the things that succeed, I still expect that they're gonna be adding co-founders, um, as the company progresses.
- JFJared Friedman
Garry, do you also want to talk about the trend towards, like, more experienced people starting companies?
- GTGarry Tan
So it does seem
- 32:54 – 34:59
Why Experienced Founders Are Back
- GTGarry Tan
like, uh, people who have been around the block a few times are doing much, much better. I think of Peter Steinberger as, like, sort of the canonical example. Like, you know, he's, uh, I believe in his early 40s, and, uh, he'd been a dev manager. He'd worked on startups before, and then, you know, he sort of uniquely got extremely AI pilled with the clankers early. But then he just tried a lot of stuff, and then he knows what to build. And so that's one thing that I think is actually really encouraging. It's like, basically, if you've been around the block, you know where the d- you know where the dragons are. You sort of, uh, have taste, and then those people in particular are, like, unusually powerful right now. Yeah, I mean, there are just so many, uh, classic gatekept things that happen. It's like, oh, you know, you have to have a co-founder. You need, like, a certain set of, you know, cool investors to be into you. And, like, now it's just less and less true. It's actually like you need to know what to build. That's like the higher order bit now is you need to know what to build. And if you've lived a little bit and you've been in places and you're very opinionated, like, actually now you might not have an excuse. Like, what's your excuse?
- HTHarj Taggar
[laughs]
- GTGarry Tan
Like, you've been this loudmouth on the internet for so long. Like, [laughs] you know, why are you not building something? Like, just pop open OpenCode and just go do it, you know? Like, put your money where your mouth is.
- JFJared Friedman
I also wonder if managing coding agents is actually, like, in some ways not that different from managing people.
- GTGarry Tan
Oh, yes.
- JFJared Friedman
And so, like, people like Peter or you or R- or Boris Cherny and, like, Toby from Shopify who, who have had whole careers, like, managing teams of engineers actually, like, take to this super well and can, like, spin up huge teams of coding agents and manage them maybe more effectively than even, like, a really smart 19-year-old who hasn't had those years of experience.
- GTGarry Tan
Yeah, we, we can be a little bit less abusive to our agents. [laughs]
- JFJared Friedman
[laughs]
- HTHarj Taggar
[laughs]
- GTGarry Tan
Try to understand where they're coming from. Uh, you have to catch their emotions, like, 99.9% less. [laughs]
- JFJared Friedman
Yeah.
- GTGarry Tan
So yeah, it's pretty helpful. [laughs]
- HTHarj Taggar
[laughs]
- DHDiana Hu
I wonder what's the
- 34:59 – 36:28
What Founders Should Do Right Now
- DHDiana Hu
concrete advice for, uh, someone that wants to get started and wanna build a company, like, right now in the current era.
- GTGarry Tan
I mean, uh, just start prompting. I mean, opening up GPT-6 today was pretty wild. I mean, just that moment where your agents are, you know, palpably smarter. They, you know, a bunch of things that you've been annoyed about, like these bugs that, you know, you haven't had time to deep, deep dive yourself. You just be like, "Actually, could you just go back to the list of things that you couldn't figure out? Like, look at your, you know, all of our last chats, and, you know, anything that looks like you didn't figure out, like, try to figure it out now." And it'll do it, like, every single time. Like, you know, it's-
- JFJared Friedman
Cool
- GTGarry Tan
... what a weird moment we are in history where you wake up in the morning, you, like, wire up a new model.
- HTHarj Taggar
Mm-hmm.
- GTGarry Tan
And then these things that even a month ago you're just like, "Why isn't it working?" It just starts working. [laughs] And, like, you know, to think that that might be this thing that we get to do for the next 18, 24 months, 36 months, like, who kn- you know, I don't know where, when it ends, but, um, that's coding in the time of AGI, I guess. Well, that's all we have time for for today. But, um, if you can't tell, we're all pretty excited about what's going on right now, and you should be too. So we can't wait to see what you build. [upbeat music]
Episode duration: 36:28
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